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EU AI Act — GPAI Model Obligations
General Purpose AI (GPAI) model providers must publish technical documentation, train data summaries, and comply with copyright transparency requirements. Companies deploying GPAI models must maintain audit logs proving compliance.
The Developer Problem
Developers integrating GPAI models (GPT-4, Claude, Gemini) into production apps must now maintain compliance documentation, audit logs, and technical disclosures — none of which existing API wrappers provide automatically.
What You Must Build
These are the exact technical components regulators will check for:
Technical documentation for every GPAI model used in production
Copyright compliance checks on training data
Incident reporting pipeline to national AI supervisory authority
Real-time audit logging of all GPAI model calls with metadata
Consequences of Non-Compliance
Fines up to €30 million or 6% global turnover for systemic risk violations
Market access suspension across all 27 EU member states
Personal liability for C-level executives signing compliance declarations
Drop-In Code Solution
Instead of building this from scratch (2–6 weeks of engineering time), use this production-ready package that implements all the required components above.
A serverless Next.js edge middleware that automatically intercepts all LLM API calls, extracts required metadata (model, prompt hash, completion hash, timestamp, user context), and writes cryptographically signed audit records to the client's own storage bucket — never to VektorIndex servers.
// audit-logger.middleware.ts
import { withAuditLog } from './audit-logger.middleware'
export const POST = withAuditLog(
async (req: Request) => {
// Your existing AI API route — unchanged
const response = await openai.chat.completions.create({ ... })
return Response.json(response)
},
{
storage: 'supabase', // or 'r2', 's3'
tableName: 'ai_audit_logs',
hashPrompts: true, // SHA-256 hash, never stores raw prompts
includeUserContext: true,
complianceMode: 'eu-ai-act' // or 'colorado', 'both'
}
)